def _get_irf_plot_config(names, impcol, rescol):
nrows = ncols = k = len(names)
if impcol is not None and rescol is not None:
# plot one impulse-response pair
nrows = ncols = 1
j = util.get_index(names, impcol)
i = util.get_index(names, rescol)
to_plot = [(j, i, 0, 0)]
elif impcol is not None:
# plot impacts of impulse in one variable
ncols = 1
j = util.get_index(names, impcol)
to_plot = [(j, i, i, 0) for i in range(k)]
elif rescol is not None:
# plot only things having impact on particular variable
ncols = 1
i = util.get_index(names, rescol)
to_plot = [(j, i, j, 0) for j in range(k)]
else:
# plot everything
to_plot = [(j, i, i, j) for i in range(k) for j in range(k)]
return nrows, ncols, to_plot
def _get_irf_plot_config(names, impcol, rescol):
nrows = ncols = k = len(names)
if impcol is not None and rescol is not None:
# plot one impulse-response pair
nrows = ncols = 1
j = util.get_index(names, impcol)
i = util.get_index(names, rescol)
to_plot = [(j, i, 0, 0)]
elif impcol is not None:
# plot impacts of impulse in one variable
ncols = 1
j = util.get_index(names, impcol)
to_plot = [(j, i, i, 0) for i in range(k)]
elif rescol is not None:
# plot only things having impact on particular variable
ncols = 1
i = util.get_index(names, rescol)
to_plot = [(j, i, j, 0) for j in range(k)]
else:
# plot everything
to_plot = [(j, i, i, j) for i in range(k) for j in range(k)]
return nrows, ncols, to_plot
def get_eq_index(self, name):
"Return integer position of requested equation name"
return util.get_index(self.names, name)
def a_test_causality(self, caused, causing=None, kind='f', signif=0.05):
if not (0 < signif < 1):
raise ValueError("signif has to be between 0 and 1")
allowed_types = (string_types, int)
if isinstance(caused, allowed_types):
caused = [caused]
if not all(isinstance(c, allowed_types) for c in caused):
raise TypeError("caused has to be of type string or int (or a "
"sequence of these types).")
caused = [self.names[c] if type(c) == int else c for c in caused]
caused_ind = [util.get_index(self.names, c) for c in caused]
if causing is not None:
if isinstance(causing, allowed_types):
causing = [causing]
if not all(isinstance(c, allowed_types) for c in causing):
raise TypeError("causing has to be of type string or int (or "
"a sequence of these types) or None.")
causing = [self.names[c] if type(c) == int else c for c in causing]
causing_ind = [util.get_index(self.names, c) for c in causing]
if causing is None:
causing_ind = [i for i in range(self.neqs) if i not in caused_ind]
causing = [self.names[c] for c in caused_ind]
k, p = self.neqs, self.k_ar
# number of restrictions
num_restr = len(causing) * len(caused) * p
num_det_terms = self.k_exog
# Make restriction matrix
C = np.zeros((num_restr, k * num_det_terms + k ** 2 * p), dtype=float)
cols_det = k * num_det_terms
row = 0
for j in range(p):
for ing_ind in causing_ind:
for ed_ind in caused_ind:
C[row, cols_det + ed_ind + k * ing_ind + k ** 2 * j] = 1
row += 1
# Lutkepohl 3.6.5
Cb = np.dot(C, vec(self.params.T))
middle = scipy.linalg.inv(chain_dot(C, self.cov_params, C.T))
# wald statistic
lam_wald = statistic = chain_dot(Cb, middle, Cb)
if kind.lower() == 'wald':
df = num_restr
dist = stats.chi2(df)
elif kind.lower() == 'f':
statistic = lam_wald / num_restr
df = (num_restr, k * self.df_resid)
dist = stats.f(*df)
else:
raise Exception('kind %s not recognized' % kind)
pvalue = dist.sf(statistic)
crit_value = dist.ppf(1 - signif)
# print(pvalue)
# print("---====--")
return pvalue, CausalityTestResults(causing, caused, statistic,
crit_value, pvalue, df, signif,
test="granger", method=kind)
